The Twitter of babel: mapping world languages through microblogging platforms
Open accessAbstract
Large scale analysis and statistics of socio-technical systems that just a few short years ago would have required the use of consistent economic and human resources can nowadays be conveniently performed by mining the enormous amount of digital data produced by human activities. Although a characterization of several aspects of our societies is emerging from the data revolution, a number of questions concerning the reliability and the biases inherent to the big data "proxies" of social life are still open. Here, we survey worldwide linguistic indicators and trends through the analysis of a large-scale dataset of microblogging posts. We show that available data allow for the study of language geography at scales ranging from country-level aggregation to specific city neighborhoods. The high resolution and coverage of the data allows us to investigate different indicators such as the linguistic homogeneity of different countries, the touristic seasonal patterns within countries and the geographical distribution of different languages in multilingual regions. This work highlights the potential of geolocalized studies of open data sources to improve current analysis and develop indicators for major social phenomena in specific communities.
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Mocanu et al. observed seasonal variations in the linguistic composition of each country, between geographical areas at different granularity scales, even at city neighborhood level.
“By mapping the use of languages across countries, Mocanu et al. (2013) observed seasonal variations in the linguistic composition of each country, as well as between geographical areas at different granularity scales, even at the level of city neighborhoods.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Mocanu et al., 2013
The Results (‘Seasonal variations’) show monthly changes in language composition for selected countries, particularly France, Italy and Spain; they do not establish seasonal variation in every country. The Introduction and Discussion separately report geographic analyses from country level down to city neighborhoods. (LLM, conf=0.84)
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- Authors
- Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A.
- Affiliations
- Northeastern University (US)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Vespignani, Alessandro
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-04-18
- Volume
- 8
- Issue
- 4
- Pages
- e61981
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3630228
- OpenAlex
- W2044734026
- Cited by
- 293
- References
- 30
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 10 · 2023: 13 · 2022: 16 · 2021: 21 · 2020: 17 · 2019: 26 · 2018: 31 · 2017: 38 · 2016: 38 · 2015: 36 · 2014: 23 · 2013: 10
- FWCI
- 151.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Greenwich Academic Literature Archive (University of Greenwich) (acceptedVersion)
- City Research Online (City University London) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1101743)
- Intelligence Advanced Research Projects Activity (D12PC00285)
- Topics
- Social Sciences › Linguistics and Language › Linguistic and Sociocultural Studies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Keywords
- dialect geography, microblogging, Twitter, multilingual regions, linguistic homogeneity, language use patterns
- MeSH terms
- Humans, Language, Linguistics, Internet, Blogging, Data Mining, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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